Penalized-Likelihood Reconstruction with High-Fidelity Measurement Models for High-Resolution Cone-Beam Imaging

Steven Tilley, Matthew Jacobson, Qian Cao, Michael Brehler, Alejandro Sisniega Crespo, Wojciech Zbijewski, Joseph Webster Stayman

Research output: Contribution to journalArticle

Abstract

We present a novel reconstruction algorithm based on a general cone-beam CT forward model which is capable of incorporating the blur and noise correlations that are exhibited in flat-panel CBCT measurement data. Specifically, the proposed model may include scintillator blur, focal-spot blur, and noise correlations due to light spread in the scintillator. The proposed algorithm (GPL-BC) uses a Gaussian Penalized- Likelihood objective function which incorporates models of Blur and Correlated noise. In a simulation study, GPL-BC was able to achieve lower bias as compared to deblurring followed by FDK as well as a model-based reconstruction method without integration of measurement blur. In the same study, GPLBC was able to achieve better line-pair reconstructions (in terms of segmented-image accuracy) as compared to deblurring followed by FDK, a model based method without blur, and a model based method with blur but not noise correlations. A prototype extremities quantitative cone-beam CT test bench was used to image a physical sample of human trabecular bone. These data were used to compare reconstructions using the proposed method and model based methods without blur and/or correlation to a registered µCT image of the same bone sample. The GPL-BC reconstructions resulted in more accurate trabecular bone segmentation. Multiple trabecular bone metrics, including Trabecular Thickness (Tb.Th.) were computed for each reconstruction approach as well as the µCT volume. The GPL-BC reconstruction provided the most accurate Tb.Th. measurement, 0.255mm, as compared to the µCT derived value of 0.193mm, followed by the GPL-B reconstruction, the GPL-I reconstruction, and then the FDK reconstruction (0.271mm, 0.309mm, and 0.335mm, respectively).

Original languageEnglish (US)
JournalIEEE Transactions on Medical Imaging
DOIs
StateAccepted/In press - Nov 30 2017

Fingerprint

Cones
Cone-Beam Computed Tomography
Noise
Imaging techniques
Bone
Phosphors
Likelihood Functions
Thickness measurement
Extremities
Light
Bone and Bones
Cancellous Bone

Keywords

  • Bones
  • Computed tomography
  • Correlation
  • Covariance matrices
  • Deconvolution
  • Extremities Imaging
  • Image reconstruction
  • Mathematical model
  • Model-based Iterative Reconstruction
  • Noise Correlation
  • Noise measurement
  • Trabecular Bone

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

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title = "Penalized-Likelihood Reconstruction with High-Fidelity Measurement Models for High-Resolution Cone-Beam Imaging",
abstract = "We present a novel reconstruction algorithm based on a general cone-beam CT forward model which is capable of incorporating the blur and noise correlations that are exhibited in flat-panel CBCT measurement data. Specifically, the proposed model may include scintillator blur, focal-spot blur, and noise correlations due to light spread in the scintillator. The proposed algorithm (GPL-BC) uses a Gaussian Penalized- Likelihood objective function which incorporates models of Blur and Correlated noise. In a simulation study, GPL-BC was able to achieve lower bias as compared to deblurring followed by FDK as well as a model-based reconstruction method without integration of measurement blur. In the same study, GPLBC was able to achieve better line-pair reconstructions (in terms of segmented-image accuracy) as compared to deblurring followed by FDK, a model based method without blur, and a model based method with blur but not noise correlations. A prototype extremities quantitative cone-beam CT test bench was used to image a physical sample of human trabecular bone. These data were used to compare reconstructions using the proposed method and model based methods without blur and/or correlation to a registered µCT image of the same bone sample. The GPL-BC reconstructions resulted in more accurate trabecular bone segmentation. Multiple trabecular bone metrics, including Trabecular Thickness (Tb.Th.) were computed for each reconstruction approach as well as the µCT volume. The GPL-BC reconstruction provided the most accurate Tb.Th. measurement, 0.255mm, as compared to the µCT derived value of 0.193mm, followed by the GPL-B reconstruction, the GPL-I reconstruction, and then the FDK reconstruction (0.271mm, 0.309mm, and 0.335mm, respectively).",
keywords = "Bones, Computed tomography, Correlation, Covariance matrices, Deconvolution, Extremities Imaging, Image reconstruction, Mathematical model, Model-based Iterative Reconstruction, Noise Correlation, Noise measurement, Trabecular Bone",
author = "Steven Tilley and Matthew Jacobson and Qian Cao and Michael Brehler and {Sisniega Crespo}, Alejandro and Wojciech Zbijewski and Stayman, {Joseph Webster}",
year = "2017",
month = "11",
day = "30",
doi = "10.1109/TMI.2017.2779406",
language = "English (US)",
journal = "IEEE Transactions on Medical Imaging",
issn = "0278-0062",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

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T1 - Penalized-Likelihood Reconstruction with High-Fidelity Measurement Models for High-Resolution Cone-Beam Imaging

AU - Tilley, Steven

AU - Jacobson, Matthew

AU - Cao, Qian

AU - Brehler, Michael

AU - Sisniega Crespo, Alejandro

AU - Zbijewski, Wojciech

AU - Stayman, Joseph Webster

PY - 2017/11/30

Y1 - 2017/11/30

N2 - We present a novel reconstruction algorithm based on a general cone-beam CT forward model which is capable of incorporating the blur and noise correlations that are exhibited in flat-panel CBCT measurement data. Specifically, the proposed model may include scintillator blur, focal-spot blur, and noise correlations due to light spread in the scintillator. The proposed algorithm (GPL-BC) uses a Gaussian Penalized- Likelihood objective function which incorporates models of Blur and Correlated noise. In a simulation study, GPL-BC was able to achieve lower bias as compared to deblurring followed by FDK as well as a model-based reconstruction method without integration of measurement blur. In the same study, GPLBC was able to achieve better line-pair reconstructions (in terms of segmented-image accuracy) as compared to deblurring followed by FDK, a model based method without blur, and a model based method with blur but not noise correlations. A prototype extremities quantitative cone-beam CT test bench was used to image a physical sample of human trabecular bone. These data were used to compare reconstructions using the proposed method and model based methods without blur and/or correlation to a registered µCT image of the same bone sample. The GPL-BC reconstructions resulted in more accurate trabecular bone segmentation. Multiple trabecular bone metrics, including Trabecular Thickness (Tb.Th.) were computed for each reconstruction approach as well as the µCT volume. The GPL-BC reconstruction provided the most accurate Tb.Th. measurement, 0.255mm, as compared to the µCT derived value of 0.193mm, followed by the GPL-B reconstruction, the GPL-I reconstruction, and then the FDK reconstruction (0.271mm, 0.309mm, and 0.335mm, respectively).

AB - We present a novel reconstruction algorithm based on a general cone-beam CT forward model which is capable of incorporating the blur and noise correlations that are exhibited in flat-panel CBCT measurement data. Specifically, the proposed model may include scintillator blur, focal-spot blur, and noise correlations due to light spread in the scintillator. The proposed algorithm (GPL-BC) uses a Gaussian Penalized- Likelihood objective function which incorporates models of Blur and Correlated noise. In a simulation study, GPL-BC was able to achieve lower bias as compared to deblurring followed by FDK as well as a model-based reconstruction method without integration of measurement blur. In the same study, GPLBC was able to achieve better line-pair reconstructions (in terms of segmented-image accuracy) as compared to deblurring followed by FDK, a model based method without blur, and a model based method with blur but not noise correlations. A prototype extremities quantitative cone-beam CT test bench was used to image a physical sample of human trabecular bone. These data were used to compare reconstructions using the proposed method and model based methods without blur and/or correlation to a registered µCT image of the same bone sample. The GPL-BC reconstructions resulted in more accurate trabecular bone segmentation. Multiple trabecular bone metrics, including Trabecular Thickness (Tb.Th.) were computed for each reconstruction approach as well as the µCT volume. The GPL-BC reconstruction provided the most accurate Tb.Th. measurement, 0.255mm, as compared to the µCT derived value of 0.193mm, followed by the GPL-B reconstruction, the GPL-I reconstruction, and then the FDK reconstruction (0.271mm, 0.309mm, and 0.335mm, respectively).

KW - Bones

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KW - Correlation

KW - Covariance matrices

KW - Deconvolution

KW - Extremities Imaging

KW - Image reconstruction

KW - Mathematical model

KW - Model-based Iterative Reconstruction

KW - Noise Correlation

KW - Noise measurement

KW - Trabecular Bone

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